Overview

Dataset statistics

Number of variables18
Number of observations4366
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory614.1 KiB
Average record size in memory144.0 B

Variable types

Numeric18

Alerts

purchases is highly correlated with quantity_p and 4 other fieldsHigh correlation
devolutions is highly correlated with recency_d and 4 other fieldsHigh correlation
recency_p is highly correlated with invoices_p and 1 other fieldsHigh correlation
recency_d is highly correlated with devolutions and 4 other fieldsHigh correlation
quantity_p is highly correlated with purchases and 4 other fieldsHigh correlation
quantity_d is highly correlated with devolutions and 4 other fieldsHigh correlation
invoices_p is highly correlated with purchases and 5 other fieldsHigh correlation
invoices_d is highly correlated with devolutions and 4 other fieldsHigh correlation
avg_ticket is highly correlated with avg_varietyHigh correlation
avg_recency_days is highly correlated with purchases and 4 other fieldsHigh correlation
avg_basket_size is highly correlated with purchases and 2 other fieldsHigh correlation
avg_variety is highly correlated with avg_ticketHigh correlation
purchases_pday is highly correlated with invoices_pHigh correlation
gross_revenue is highly correlated with purchases and 4 other fieldsHigh correlation
relative_revenue is highly correlated with devolutions and 4 other fieldsHigh correlation
relative_quantity is highly correlated with devolutions and 4 other fieldsHigh correlation
purchases is highly correlated with quantity_p and 2 other fieldsHigh correlation
devolutions is highly correlated with quantity_p and 3 other fieldsHigh correlation
recency_p is highly correlated with avg_recency_daysHigh correlation
recency_d is highly correlated with invoices_dHigh correlation
quantity_p is highly correlated with purchases and 4 other fieldsHigh correlation
quantity_d is highly correlated with devolutions and 3 other fieldsHigh correlation
invoices_p is highly correlated with purchases and 2 other fieldsHigh correlation
invoices_d is highly correlated with recency_d and 1 other fieldsHigh correlation
avg_ticket is highly correlated with devolutions and 3 other fieldsHigh correlation
avg_recency_days is highly correlated with recency_p and 1 other fieldsHigh correlation
avg_basket_size is highly correlated with devolutions and 3 other fieldsHigh correlation
purchases_pday is highly correlated with avg_recency_daysHigh correlation
gross_revenue is highly correlated with purchases and 1 other fieldsHigh correlation
relative_revenue is highly correlated with relative_quantityHigh correlation
relative_quantity is highly correlated with relative_revenueHigh correlation
purchases is highly correlated with quantity_p and 2 other fieldsHigh correlation
devolutions is highly correlated with recency_d and 4 other fieldsHigh correlation
recency_p is highly correlated with avg_recency_daysHigh correlation
recency_d is highly correlated with devolutions and 4 other fieldsHigh correlation
quantity_p is highly correlated with purchases and 2 other fieldsHigh correlation
quantity_d is highly correlated with devolutions and 4 other fieldsHigh correlation
invoices_p is highly correlated with purchases and 2 other fieldsHigh correlation
invoices_d is highly correlated with devolutions and 4 other fieldsHigh correlation
avg_recency_days is highly correlated with recency_p and 1 other fieldsHigh correlation
avg_basket_size is highly correlated with quantity_pHigh correlation
gross_revenue is highly correlated with purchases and 2 other fieldsHigh correlation
relative_revenue is highly correlated with devolutions and 4 other fieldsHigh correlation
relative_quantity is highly correlated with devolutions and 4 other fieldsHigh correlation
df_index is highly correlated with recency_p and 1 other fieldsHigh correlation
purchases is highly correlated with devolutions and 7 other fieldsHigh correlation
devolutions is highly correlated with purchases and 4 other fieldsHigh correlation
recency_p is highly correlated with df_index and 1 other fieldsHigh correlation
quantity_p is highly correlated with purchases and 5 other fieldsHigh correlation
quantity_d is highly correlated with purchases and 4 other fieldsHigh correlation
invoices_p is highly correlated with purchases and 2 other fieldsHigh correlation
invoices_d is highly correlated with purchases and 2 other fieldsHigh correlation
avg_ticket is highly correlated with purchases and 4 other fieldsHigh correlation
avg_recency_days is highly correlated with df_index and 1 other fieldsHigh correlation
avg_basket_size is highly correlated with purchases and 4 other fieldsHigh correlation
gross_revenue is highly correlated with purchases and 3 other fieldsHigh correlation
relative_revenue is highly correlated with relative_quantityHigh correlation
relative_quantity is highly correlated with relative_revenueHigh correlation
devolutions is highly skewed (γ1 = 47.38221518) Skewed
quantity_p is highly skewed (γ1 = 30.93123636) Skewed
quantity_d is highly skewed (γ1 = 45.51559535) Skewed
avg_ticket is highly skewed (γ1 = 46.64636797) Skewed
avg_basket_size is highly skewed (γ1 = 48.13011902) Skewed
gross_revenue is highly skewed (γ1 = 21.69086914) Skewed
df_index has unique values Unique
customer_id has unique values Unique
devolutions has 2778 (63.6%) zeros Zeros
quantity_d has 2778 (63.6%) zeros Zeros
invoices_d has 2778 (63.6%) zeros Zeros

Reproduction

Analysis started2022-02-04 18:20:05.289728
Analysis finished2022-02-04 18:20:44.647390
Duration39.36 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct4366
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2837.469079
Minimum0
Maximum5970
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2022-02-04T15:20:44.739987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile229.25
Q11303.25
median2733.5
Q34423.75
95-th percentile5635.75
Maximum5970
Range5970
Interquartile range (IQR)3120.5

Descriptive statistics

Standard deviation1758.395649
Coefficient of variation (CV)0.6197056603
Kurtosis-1.238510832
Mean2837.469079
Median Absolute Deviation (MAD)1550
Skewness0.1035389668
Sum12388390
Variance3091955.26
MonotonicityStrictly increasing
2022-02-04T15:20:44.854319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
37991
 
< 0.1%
38051
 
< 0.1%
38041
 
< 0.1%
38031
 
< 0.1%
38021
 
< 0.1%
38011
 
< 0.1%
38001
 
< 0.1%
37941
 
< 0.1%
39661
 
< 0.1%
Other values (4356)4356
99.8%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
59701
< 0.1%
59631
< 0.1%
59621
< 0.1%
59601
< 0.1%
59581
< 0.1%
59541
< 0.1%
59531
< 0.1%
59521
< 0.1%
59511
< 0.1%
59501
< 0.1%

customer_id
Real number (ℝ≥0)

UNIQUE

Distinct4366
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15301.45121
Minimum12346
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2022-02-04T15:20:44.974845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum12346
5-th percentile12614.25
Q113814.25
median15303.5
Q316779.75
95-th percentile17984.75
Maximum18287
Range5941
Interquartile range (IQR)2965.5

Descriptive statistics

Standard deviation1722.144646
Coefficient of variation (CV)0.1125477984
Kurtosis-1.195850103
Mean15301.45121
Median Absolute Deviation (MAD)1484.5
Skewness0.0001007032311
Sum66806136
Variance2965782.181
MonotonicityNot monotonic
2022-02-04T15:20:45.103948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
159091
 
< 0.1%
124501
 
< 0.1%
153341
 
< 0.1%
175621
 
< 0.1%
178791
 
< 0.1%
160501
 
< 0.1%
136181
 
< 0.1%
168691
 
< 0.1%
137301
 
< 0.1%
Other values (4356)4356
99.8%
ValueCountFrequency (%)
123461
< 0.1%
123471
< 0.1%
123481
< 0.1%
123491
< 0.1%
123501
< 0.1%
123521
< 0.1%
123531
< 0.1%
123541
< 0.1%
123551
< 0.1%
123561
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182811
< 0.1%
182801
< 0.1%
182781
< 0.1%
182771
< 0.1%
182761
< 0.1%
182741
< 0.1%
182731
< 0.1%

purchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4249
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2040.183367
Minimum0
Maximum280206.02
Zeros33
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2022-02-04T15:20:45.251597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile107.4875
Q1303.3075
median665.82
Q31654.0875
95-th percentile5775.52
Maximum280206.02
Range280206.02
Interquartile range (IQR)1350.78

Descriptive statistics

Standard deviation8962.013605
Coefficient of variation (CV)4.392749078
Kurtosis480.9126511
Mean2040.183367
Median Absolute Deviation (MAD)465.995
Skewness19.38102887
Sum8907440.58
Variance80317687.85
MonotonicityNot monotonic
2022-02-04T15:20:45.414968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
033
 
0.8%
76.324
 
0.1%
4403
 
0.1%
35.43
 
0.1%
153
 
0.1%
363.653
 
0.1%
852
 
< 0.1%
122.72
 
< 0.1%
110.382
 
< 0.1%
238.852
 
< 0.1%
Other values (4239)4309
98.7%
ValueCountFrequency (%)
033
0.8%
3.751
 
< 0.1%
6.21
 
< 0.1%
6.91
 
< 0.1%
12.751
 
< 0.1%
13.31
 
< 0.1%
153
 
0.1%
171
 
< 0.1%
20.82
 
< 0.1%
25.52
 
< 0.1%
ValueCountFrequency (%)
280206.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
168472.51
< 0.1%
143825.061
< 0.1%
124914.531
< 0.1%
117379.631
< 0.1%
91062.381
< 0.1%
81024.841
< 0.1%
77183.61
< 0.1%

devolutions
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct1156
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140.019787
Minimum0
Maximum168469.6
Zeros2778
Zeros (%)63.6%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2022-02-04T15:20:45.558387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q316.575
95-th percentile191.135
Maximum168469.6
Range168469.6
Interquartile range (IQR)16.575

Descriptive statistics

Standard deviation2954.51808
Coefficient of variation (CV)21.10071829
Kurtosis2530.699539
Mean140.019787
Median Absolute Deviation (MAD)0
Skewness47.38221518
Sum611326.39
Variance8729177.088
MonotonicityNot monotonic
2022-02-04T15:20:45.682214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02778
63.6%
12.7521
 
0.5%
4.9518
 
0.4%
1515
 
0.3%
9.9515
 
0.3%
5.912
 
0.3%
25.511
 
0.3%
4.2510
 
0.2%
3.759
 
0.2%
19.99
 
0.2%
Other values (1146)1468
33.6%
ValueCountFrequency (%)
02778
63.6%
0.422
 
< 0.1%
0.651
 
< 0.1%
0.771
 
< 0.1%
0.951
 
< 0.1%
1.255
 
0.1%
1.454
 
0.1%
1.641
 
< 0.1%
1.655
 
0.1%
1.72
 
< 0.1%
ValueCountFrequency (%)
168469.61
< 0.1%
77183.61
< 0.1%
392671
< 0.1%
30032.231
< 0.1%
22998.41
< 0.1%
12158.91
< 0.1%
11252.441
< 0.1%
8593.151
< 0.1%
8495.011
< 0.1%
8043.881
< 0.1%

recency_p
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct304
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.07558406
Minimum0
Maximum373
Zeros35
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2022-02-04T15:20:45.810178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q117
median51
Q3147
95-th percentile318
Maximum373
Range373
Interquartile range (IQR)130

Descriptive statistics

Standard deviation102.4445304
Coefficient of variation (CV)1.088959812
Kurtosis0.3775336914
Mean94.07558406
Median Absolute Deviation (MAD)41
Skewness1.235556949
Sum410734
Variance10494.8818
MonotonicityNot monotonic
2022-02-04T15:20:45.919016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1103
 
2.4%
394
 
2.2%
494
 
2.2%
290
 
2.1%
879
 
1.8%
1077
 
1.8%
1774
 
1.7%
772
 
1.6%
971
 
1.6%
2264
 
1.5%
Other values (294)3548
81.3%
ValueCountFrequency (%)
035
 
0.8%
1103
2.4%
290
2.1%
394
2.2%
494
2.2%
548
1.1%
772
1.6%
879
1.8%
971
1.6%
1077
1.8%
ValueCountFrequency (%)
37317
 
0.4%
37218
0.4%
3716
 
0.1%
3693
 
0.1%
3685
 
0.1%
3675
 
0.1%
36610
 
0.2%
36543
1.0%
3646
 
0.1%
3626
 
0.1%

recency_d
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct278
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean280.8964727
Minimum0
Maximum373
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2022-02-04T15:20:46.045546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18
Q1185
median365
Q3365
95-th percentile365
Maximum373
Range373
Interquartile range (IQR)180

Descriptive statistics

Standard deviation129.9871958
Coefficient of variation (CV)0.4627583768
Kurtosis-0.4666517161
Mean280.8964727
Median Absolute Deviation (MAD)0
Skewness-1.118539076
Sum1226394
Variance16896.67107
MonotonicityNot monotonic
2022-02-04T15:20:46.170912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3652792
63.9%
844
 
1.0%
6439
 
0.9%
3528
 
0.6%
328
 
0.6%
926
 
0.6%
2126
 
0.6%
2522
 
0.5%
2922
 
0.5%
3120
 
0.5%
Other values (268)1319
30.2%
ValueCountFrequency (%)
05
 
0.1%
120
0.5%
213
 
0.3%
328
0.6%
413
 
0.3%
54
 
0.1%
710
 
0.2%
844
1.0%
926
0.6%
108
 
0.2%
ValueCountFrequency (%)
3731
 
< 0.1%
3728
 
0.2%
3712
 
< 0.1%
3692
 
< 0.1%
3688
 
0.2%
3671
 
< 0.1%
3667
 
0.2%
3652792
63.9%
3641
 
< 0.1%
3622
 
< 0.1%

quantity_p
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct777
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean311.8268438
Minimum0
Maximum80996
Zeros33
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2022-02-04T15:20:46.295244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18
Q160
median115
Q3234
95-th percentile710
Maximum80996
Range80996
Interquartile range (IQR)174

Descriptive statistics

Standard deviation1965.923119
Coefficient of variation (CV)6.304534579
Kurtosis1148.530998
Mean311.8268438
Median Absolute Deviation (MAD)69
Skewness30.93123636
Sum1361436
Variance3864853.711
MonotonicityNot monotonic
2022-02-04T15:20:46.415749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2842
 
1.0%
6040
 
0.9%
6739
 
0.9%
5137
 
0.8%
7236
 
0.8%
5235
 
0.8%
6634
 
0.8%
7033
 
0.8%
033
 
0.8%
3632
 
0.7%
Other values (767)4005
91.7%
ValueCountFrequency (%)
033
0.8%
111
 
0.3%
25
 
0.1%
314
0.3%
46
 
0.1%
52
 
< 0.1%
614
0.3%
73
 
0.1%
85
 
0.1%
98
 
0.2%
ValueCountFrequency (%)
809961
< 0.1%
742151
< 0.1%
386391
< 0.1%
213521
< 0.1%
173761
< 0.1%
171501
< 0.1%
162881
< 0.1%
158531
< 0.1%
133691
< 0.1%
128721
< 0.1%

quantity_d
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct186
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.71071919
Minimum-0
Maximum80995
Zeros2778
Zeros (%)63.6%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2022-02-04T15:20:46.536945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0
5-th percentile0
Q10
median-0
Q33
95-th percentile42
Maximum80995
Range80995
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1678.948683
Coefficient of variation (CV)30.13690556
Kurtosis2109.732802
Mean55.71071919
Median Absolute Deviation (MAD)0
Skewness45.51559535
Sum243233
Variance2818868.681
MonotonicityNot monotonic
2022-02-04T15:20:46.650284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-02778
63.6%
1349
 
8.0%
3173
 
4.0%
690
 
2.1%
289
 
2.0%
475
 
1.7%
545
 
1.0%
1245
 
1.0%
742
 
1.0%
840
 
0.9%
Other values (176)640
 
14.7%
ValueCountFrequency (%)
-02778
63.6%
1349
 
8.0%
289
 
2.0%
3173
 
4.0%
475
 
1.7%
545
 
1.0%
690
 
2.1%
742
 
1.0%
840
 
0.9%
937
 
0.8%
ValueCountFrequency (%)
809951
< 0.1%
742151
< 0.1%
93611
< 0.1%
90141
< 0.1%
48731
< 0.1%
40271
< 0.1%
23991
< 0.1%
23021
< 0.1%
21601
< 0.1%
16851
< 0.1%

invoices_p
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct60
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.241868988
Minimum0
Maximum209
Zeros33
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2022-02-04T15:20:46.766628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q35
95-th percentile13
Maximum209
Range209
Interquartile range (IQR)4

Descriptive statistics

Standard deviation7.681885185
Coefficient of variation (CV)1.810967101
Kurtosis249.7371905
Mean4.241868988
Median Absolute Deviation (MAD)1
Skewness12.0756929
Sum18520
Variance59.01135999
MonotonicityNot monotonic
2022-02-04T15:20:46.873982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11493
34.2%
2831
19.0%
3508
 
11.6%
4387
 
8.9%
5242
 
5.5%
6172
 
3.9%
7143
 
3.3%
898
 
2.2%
968
 
1.6%
1054
 
1.2%
Other values (50)370
 
8.5%
ValueCountFrequency (%)
033
 
0.8%
11493
34.2%
2831
19.0%
3508
 
11.6%
4387
 
8.9%
5242
 
5.5%
6172
 
3.9%
7143
 
3.3%
898
 
2.2%
968
 
1.6%
ValueCountFrequency (%)
2091
< 0.1%
2011
< 0.1%
1241
< 0.1%
971
< 0.1%
931
< 0.1%
911
< 0.1%
861
< 0.1%
731
< 0.1%
631
< 0.1%
621
< 0.1%

invoices_d
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct27
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8364635822
Minimum0
Maximum47
Zeros2778
Zeros (%)63.6%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2022-02-04T15:20:46.973114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum47
Range47
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.136425755
Coefficient of variation (CV)2.55411688
Kurtosis134.1595049
Mean0.8364635822
Median Absolute Deviation (MAD)0
Skewness8.846668074
Sum3652
Variance4.564315005
MonotonicityNot monotonic
2022-02-04T15:20:47.065109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
02778
63.6%
1886
 
20.3%
2308
 
7.1%
3147
 
3.4%
497
 
2.2%
544
 
1.0%
630
 
0.7%
722
 
0.5%
89
 
0.2%
97
 
0.2%
Other values (17)38
 
0.9%
ValueCountFrequency (%)
02778
63.6%
1886
 
20.3%
2308
 
7.1%
3147
 
3.4%
497
 
2.2%
544
 
1.0%
630
 
0.7%
722
 
0.5%
89
 
0.2%
97
 
0.2%
ValueCountFrequency (%)
471
< 0.1%
451
< 0.1%
351
< 0.1%
311
< 0.1%
271
< 0.1%
231
< 0.1%
211
< 0.1%
192
< 0.1%
181
< 0.1%
172
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct4292
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.89335507
Minimum0
Maximum77183.6
Zeros33
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2022-02-04T15:20:47.176426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.356321839
Q111.99768757
median17.67979201
Q324.79484848
95-th percentile93.3
Maximum77183.6
Range77183.6
Interquartile range (IQR)12.79716091

Descriptive statistics

Standard deviation1463.214147
Coefficient of variation (CV)21.55165473
Kurtosis2263.833843
Mean67.89335507
Median Absolute Deviation (MAD)6.449171759
Skewness46.64636797
Sum296422.3883
Variance2140995.64
MonotonicityNot monotonic
2022-02-04T15:20:47.292112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
033
 
0.8%
155
 
0.1%
76.324
 
0.1%
25.54
 
0.1%
1794
 
0.1%
18.73
 
0.1%
3582
 
< 0.1%
13.592
 
< 0.1%
15.1742
 
< 0.1%
71.42
 
< 0.1%
Other values (4282)4305
98.6%
ValueCountFrequency (%)
033
0.8%
2.1012857141
 
< 0.1%
2.1505882351
 
< 0.1%
2.2411
 
< 0.1%
2.2643751
 
< 0.1%
2.43251
 
< 0.1%
2.4623711341
 
< 0.1%
2.5048760331
 
< 0.1%
2.508371561
 
< 0.1%
2.547049181
 
< 0.1%
ValueCountFrequency (%)
77183.61
< 0.1%
56157.51
< 0.1%
13305.51
< 0.1%
4453.431
< 0.1%
38611
< 0.1%
30961
< 0.1%
2033.11
< 0.1%
2027.861
< 0.1%
1687.21
< 0.1%
1377.0777781
< 0.1%

avg_recency_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1014
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.82100731
Minimum0
Maximum373
Zeros38
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2022-02-04T15:20:47.410856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q13.75
median19.25
Q378
95-th percentile298.75
Maximum373
Range373
Interquartile range (IQR)74.25

Descriptive statistics

Standard deviation95.0274444
Coefficient of variation (CV)1.46599765
Kurtosis2.196536983
Mean64.82100731
Median Absolute Deviation (MAD)18.25
Skewness1.794820946
Sum283008.5179
Variance9030.215189
MonotonicityNot monotonic
2022-02-04T15:20:47.529842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
161
 
1.4%
252
 
1.2%
442
 
1.0%
340
 
0.9%
038
 
0.9%
0.535
 
0.8%
533
 
0.8%
2532
 
0.7%
831
 
0.7%
729
 
0.7%
Other values (1004)3973
91.0%
ValueCountFrequency (%)
038
0.9%
0.0068493150681
 
< 0.1%
0.0088495575221
 
< 0.1%
0.011235955061
 
< 0.1%
0.018181818181
 
< 0.1%
0.018867924531
 
< 0.1%
0.021276595741
 
< 0.1%
0.021739130431
 
< 0.1%
0.024096385541
 
< 0.1%
0.0252
 
< 0.1%
ValueCountFrequency (%)
37315
0.3%
37217
0.4%
3717
0.2%
3693
 
0.1%
3685
 
0.1%
3675
 
0.1%
3668
0.2%
36510
0.2%
3645
 
0.1%
3626
 
0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct2136
Distinct (%)48.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean251.6187465
Minimum0
Maximum74215
Zeros33
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2022-02-04T15:20:47.665079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30.54166667
Q191
median160.5666667
Q3270.25
95-th percentile598.6
Maximum74215
Range74215
Interquartile range (IQR)179.25

Descriptive statistics

Standard deviation1308.868296
Coefficient of variation (CV)5.201791654
Kurtosis2541.880608
Mean251.6187465
Median Absolute Deviation (MAD)81.65
Skewness48.13011902
Sum1098567.447
Variance1713136.215
MonotonicityNot monotonic
2022-02-04T15:20:47.790566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
033
 
0.8%
10019
 
0.4%
8218
 
0.4%
13617
 
0.4%
12017
 
0.4%
8817
 
0.4%
7217
 
0.4%
10616
 
0.4%
7316
 
0.4%
7816
 
0.4%
Other values (2126)4180
95.7%
ValueCountFrequency (%)
033
0.8%
16
 
0.1%
1.51
 
< 0.1%
25
 
0.1%
32
 
< 0.1%
3.3333333331
 
< 0.1%
48
 
0.2%
53
 
0.1%
5.3333333331
 
< 0.1%
5.6666666671
 
< 0.1%
ValueCountFrequency (%)
742151
< 0.1%
40498.51
< 0.1%
78241
< 0.1%
6009.3333331
< 0.1%
43001
< 0.1%
42801
< 0.1%
3684.476191
< 0.1%
30281
< 0.1%
29241
< 0.1%
28801
< 0.1%

avg_variety
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1043
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.0160071
Minimum0
Maximum300.6470588
Zeros33
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2022-02-04T15:20:47.921262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.229166667
Q19.186363636
median16.87301587
Q328
95-th percentile59
Maximum300.6470588
Range300.6470588
Interquartile range (IQR)18.81363636

Descriptive statistics

Standard deviation20.31141942
Coefficient of variation (CV)0.9225750757
Kurtosis18.74952384
Mean22.0160071
Median Absolute Deviation (MAD)8.873015873
Skewness3.031567868
Sum96121.88702
Variance412.5537589
MonotonicityNot monotonic
2022-02-04T15:20:48.025215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199
 
2.3%
1390
 
2.1%
1484
 
1.9%
1082
 
1.9%
1181
 
1.9%
978
 
1.8%
675
 
1.7%
774
 
1.7%
571
 
1.6%
869
 
1.6%
Other values (1033)3563
81.6%
ValueCountFrequency (%)
033
 
0.8%
199
2.3%
1.21
 
< 0.1%
1.251
 
< 0.1%
1.3333333332
 
< 0.1%
1.59
 
0.2%
1.5555555561
 
< 0.1%
1.5714285711
 
< 0.1%
1.6666666674
 
0.1%
1.8333333331
 
< 0.1%
ValueCountFrequency (%)
300.64705881
< 0.1%
2191
< 0.1%
203.51
< 0.1%
1911
< 0.1%
1711
< 0.1%
1641
< 0.1%
1581
< 0.1%
1571
< 0.1%
1531
< 0.1%
1491
< 0.1%

purchases_pday
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1243
Distinct (%)28.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4005636754
Minimum0
Maximum17
Zeros33
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2022-02-04T15:20:48.145128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.009592344556
Q10.01989485241
median0.04474578328
Q31
95-th percentile1
Maximum17
Range17
Interquartile range (IQR)0.9801051476

Descriptive statistics

Standard deviation0.5606445071
Coefficient of variation (CV)1.399638913
Kurtosis175.8260771
Mean0.4005636754
Median Absolute Deviation (MAD)0.03328437113
Skewness6.66821218
Sum1748.861007
Variance0.3143222633
MonotonicityNot monotonic
2022-02-04T15:20:48.269869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11502
34.4%
250
 
1.1%
033
 
0.8%
0.062518
 
0.4%
0.0277777777817
 
0.4%
0.0238095238117
 
0.4%
0.0909090909115
 
0.3%
0.0833333333314
 
0.3%
0.0212765957413
 
0.3%
0.0344827586213
 
0.3%
Other values (1233)2674
61.2%
ValueCountFrequency (%)
033
0.8%
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054794520551
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055865921792
 
< 0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
 
< 0.1%
0.005665722381
 
< 0.1%
0.0056818181822
 
< 0.1%
ValueCountFrequency (%)
171
 
< 0.1%
42
 
< 0.1%
34
 
0.1%
250
 
1.1%
1.1428571431
 
< 0.1%
11502
34.4%
0.751
 
< 0.1%
0.66666666674
 
0.1%
0.55882352941
 
< 0.1%
0.53887399461
 
< 0.1%

gross_revenue
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct4282
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1900.16358
Minimum-4287.63
Maximum279489.02
Zeros11
Zeros (%)0.3%
Negative41
Negative (%)0.9%
Memory size34.2 KiB
2022-02-04T15:20:48.409798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-4287.63
5-th percentile101.175
Q1293.1875
median648.55
Q31612.625
95-th percentile5632.72
Maximum279489.02
Range283776.65
Interquartile range (IQR)1319.4375

Descriptive statistics

Standard deviation8224.856057
Coefficient of variation (CV)4.328498948
Kurtosis606.3365532
Mean1900.16358
Median Absolute Deviation (MAD)455.265
Skewness21.69086914
Sum8296114.19
Variance67648257.16
MonotonicityNot monotonic
2022-02-04T15:20:48.526278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
011
 
0.3%
76.324
 
0.1%
153
 
0.1%
4403
 
0.1%
35.43
 
0.1%
363.653
 
0.1%
321.052
 
< 0.1%
181.092
 
< 0.1%
1792
 
< 0.1%
73.52
 
< 0.1%
Other values (4272)4331
99.2%
ValueCountFrequency (%)
-4287.631
< 0.1%
-1592.491
< 0.1%
-1192.21
< 0.1%
-1165.31
< 0.1%
-11261
< 0.1%
-840.761
< 0.1%
-611.861
< 0.1%
-451.421
< 0.1%
-295.091
< 0.1%
-227.441
< 0.1%
ValueCountFrequency (%)
279489.021
< 0.1%
256438.491
< 0.1%
187482.171
< 0.1%
132572.621
< 0.1%
123725.451
< 0.1%
113384.141
< 0.1%
88125.381
< 0.1%
65892.081
< 0.1%
62653.11
< 0.1%
59419.341
< 0.1%

relative_revenue
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1547
Distinct (%)35.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9464585758
Minimum-1
Maximum1
Zeros11
Zeros (%)0.3%
Negative41
Negative (%)0.9%
Memory size34.2 KiB
2022-02-04T15:20:48.664674image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0.7752317671
Q10.9773601379
median1
Q31
95-th percentile1
Maximum1
Range2
Interquartile range (IQR)0.02263986212

Descriptive statistics

Standard deviation0.2102841969
Coefficient of variation (CV)0.2221800322
Kurtosis58.90281829
Mean0.9464585758
Median Absolute Deviation (MAD)0
Skewness-7.214265949
Sum4132.238142
Variance0.04421944345
MonotonicityNot monotonic
2022-02-04T15:20:48.799379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12778
63.6%
-133
 
0.8%
011
 
0.3%
0.92887985781
 
< 0.1%
0.97657441291
 
< 0.1%
0.5538013071
 
< 0.1%
0.7515580531
 
< 0.1%
0.98307456851
 
< 0.1%
0.97307364751
 
< 0.1%
0.79542170511
 
< 0.1%
Other values (1537)1537
35.2%
ValueCountFrequency (%)
-133
0.8%
-0.9655473381
 
< 0.1%
-0.59614066321
 
< 0.1%
-0.40645828551
 
< 0.1%
-0.37129840551
 
< 0.1%
-0.33333333331
 
< 0.1%
-0.25619834711
 
< 0.1%
-0.16085899511
 
< 0.1%
-0.057435203531
 
< 0.1%
011
 
0.3%
ValueCountFrequency (%)
12778
63.6%
0.99983616721
 
< 0.1%
0.99968637181
 
< 0.1%
0.99944877831
 
< 0.1%
0.99941624421
 
< 0.1%
0.99934617991
 
< 0.1%
0.99923023941
 
< 0.1%
0.9990853581
 
< 0.1%
0.99900015071
 
< 0.1%
0.99864060471
 
< 0.1%

relative_quantity
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1127
Distinct (%)25.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9273829918
Minimum-1
Maximum1
Zeros18
Zeros (%)0.4%
Negative47
Negative (%)1.1%
Memory size34.2 KiB
2022-02-04T15:20:48.937668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0.6008860448
Q10.9646793976
median1
Q31
95-th percentile1
Maximum1
Range2
Interquartile range (IQR)0.03532060239

Descriptive statistics

Standard deviation0.2302652918
Coefficient of variation (CV)0.2482957892
Kurtosis40.57015379
Mean0.9273829918
Median Absolute Deviation (MAD)0
Skewness-5.825843262
Sum4048.954142
Variance0.05302210461
MonotonicityNot monotonic
2022-02-04T15:20:49.062218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12778
63.6%
-133
 
0.8%
018
 
0.4%
0.69
 
0.2%
0.98305084758
 
0.2%
0.89473684217
 
0.2%
0.95121951227
 
0.2%
0.97183098597
 
0.2%
0.96721311487
 
0.2%
0.95744680856
 
0.1%
Other values (1117)1486
34.0%
ValueCountFrequency (%)
-133
0.8%
-0.98952879581
 
< 0.1%
-0.98742138361
 
< 0.1%
-0.5036496351
 
< 0.1%
-0.29787234041
 
< 0.1%
-0.25463743681
 
< 0.1%
-0.18231765081
 
< 0.1%
-0.13636363641
 
< 0.1%
-0.11940298511
 
< 0.1%
-0.11801242241
 
< 0.1%
ValueCountFrequency (%)
12778
63.6%
0.99984463611
 
< 0.1%
0.99938949941
 
< 0.1%
0.99931491211
 
< 0.1%
0.99928901531
 
< 0.1%
0.99903567981
 
< 0.1%
0.99871299871
 
< 0.1%
0.99866666671
 
< 0.1%
0.99861303741
 
< 0.1%
0.99853049231
 
< 0.1%

Interactions

2022-02-04T15:20:41.864652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-04T15:20:07.343516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-04T15:20:09.393625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-04T15:20:11.301561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-04T15:20:13.230025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-04T15:20:15.315973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-04T15:20:17.385397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-04T15:20:19.305672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-04T15:20:21.335086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-04T15:20:23.767891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-04T15:20:25.598127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-04T15:20:27.674197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-04T15:20:29.685863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-04T15:20:31.633965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-02-04T15:20:43.737999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-04T15:20:08.982142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-04T15:20:10.766187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-02-04T15:20:33.825543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-04T15:20:35.785853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-04T15:20:37.763623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-02-04T15:20:41.753536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-02-04T15:20:49.212324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-04T15:20:49.412151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-04T15:20:49.595339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-04T15:20:49.779251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-04T15:20:44.281708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-04T15:20:44.546635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idpurchasesdevolutionsrecency_precency_dquantity_pquantity_dinvoices_pinvoices_davg_ticketavg_recency_daysavg_basket_sizeavg_varietypurchases_pdaygross_revenuerelative_revenuerelative_quantity
00178505.391210e+031.025800e+023.720000e+023.020000e+023.500000e+012.100000e+013.400000e+011.000000e+001.815222e+011.006667e+025.097059e+018.735294e+001.700000e+015.288630e+039.626560e-012.500000e-01
11130473.237540e+031.584400e+023.100000e+013.100000e+011.320000e+026.000000e+001.000000e+018.000000e+001.882291e+012.214286e+001.391000e+021.720000e+012.915452e-023.079100e+039.066897e-019.130435e-01
22125837.281380e+039.404000e+012.000000e+005.600000e+011.569000e+035.000000e+011.500000e+013.000000e+002.947927e+011.111111e-013.373333e+021.646667e+014.032258e-027.187340e+039.744991e-019.382335e-01
33137489.482500e+020.000000e+009.500000e+013.650000e+021.690000e+02-0.000000e+005.000000e+000.000000e+003.386607e+012.375000e+018.780000e+015.600000e+001.792115e-029.482500e+021.000000e+001.000000e+00
44151008.760000e+022.409000e+023.330000e+023.300000e+024.800000e+012.200000e+013.000000e+003.000000e+002.920000e+025.500000e+012.666667e+011.000000e+007.317073e-026.351000e+025.686275e-013.714286e-01
55152914.668300e+037.179000e+012.500000e+011.720000e+025.080000e+022.700000e+011.500000e+015.000000e+004.532330e+011.470588e+001.402000e+026.866667e+004.297994e-024.596510e+039.697094e-018.990654e-01
66146885.630870e+035.234900e+027.000000e+007.000000e+005.790000e+022.810000e+022.100000e+016.000000e+001.721979e+013.333333e-011.724286e+021.557143e+015.722071e-025.107380e+038.298800e-013.465116e-01
77178095.411910e+037.842900e+021.600000e+011.600000e+019.610000e+024.100000e+011.200000e+013.000000e+008.871984e+011.333333e+001.714167e+025.083333e+003.351955e-024.627620e+037.468481e-019.181637e-01
88153116.076790e+041.348560e+030.000000e+000.000000e+002.167000e+032.310000e+029.100000e+012.700000e+012.554346e+010.000000e+004.197143e+022.614286e+012.433155e-015.941934e+049.565796e-018.073394e-01
99145278.508820e+037.974400e+022.000000e+008.000000e+001.980000e+023.000000e+005.500000e+013.100000e+018.753930e+003.125000e-023.798182e+011.767273e+011.494565e-017.711380e+038.286229e-019.701493e-01

Last rows

df_indexcustomer_idpurchasesdevolutionsrecency_precency_dquantity_pquantity_dinvoices_pinvoices_davg_ticketavg_recency_daysavg_basket_sizeavg_varietypurchases_pdaygross_revenuerelative_revenuerelative_quantity
43565950160001.239370e+040.000000e+002.000000e+003.650000e+027.700000e+02-0.000000e+003.000000e+000.000000e+001.377078e+032.000000e+001.703333e+033.000000e+003.000000e+001.239370e+041.000000e+001.000000e+00
43575951151953.861000e+030.000000e+002.000000e+003.650000e+021.404000e+03-0.000000e+001.000000e+000.000000e+003.861000e+032.000000e+001.404000e+031.000000e+001.000000e+003.861000e+031.000000e+001.000000e+00
43585952140871.944200e+021.275000e+012.000000e+002.000000e+001.130000e+021.000000e+001.000000e+001.000000e+002.817681e+002.000000e+002.510000e+026.900000e+011.000000e+001.816700e+028.769127e-019.824561e-01
43595953142041.610300e+020.000000e+002.000000e+003.650000e+022.100000e+01-0.000000e+001.000000e+000.000000e+003.659773e+002.000000e+008.200000e+014.400000e+011.000000e+001.610300e+021.000000e+001.000000e+00
43605954154714.694800e+020.000000e+002.000000e+003.650000e+021.020000e+02-0.000000e+001.000000e+000.000000e+006.097143e+002.000000e+002.660000e+027.700000e+011.000000e+004.694800e+021.000000e+001.000000e+00
43615958134361.968900e+020.000000e+001.000000e+003.650000e+025.800000e+01-0.000000e+001.000000e+000.000000e+001.640750e+011.000000e+007.600000e+011.200000e+011.000000e+001.968900e+021.000000e+001.000000e+00
43625960155203.435000e+020.000000e+001.000000e+003.650000e+021.340000e+02-0.000000e+001.000000e+000.000000e+001.908333e+011.000000e+003.140000e+021.800000e+011.000000e+003.435000e+021.000000e+001.000000e+00
43635962132983.600000e+020.000000e+001.000000e+003.650000e+029.600000e+01-0.000000e+001.000000e+000.000000e+001.800000e+021.000000e+009.600000e+012.000000e+001.000000e+003.600000e+021.000000e+001.000000e+00
43645963145692.273900e+020.000000e+001.000000e+003.650000e+027.000000e+01-0.000000e+001.000000e+000.000000e+001.894917e+011.000000e+007.900000e+011.200000e+011.000000e+002.273900e+021.000000e+001.000000e+00
43655970127138.485500e+020.000000e+000.000000e+003.650000e+021.010000e+02-0.000000e+001.000000e+000.000000e+002.233026e+010.000000e+005.080000e+023.800000e+011.000000e+008.485500e+021.000000e+001.000000e+00